Llama-3.1-8B

Meta Llama 3.1 8B is an impressive and incredibly popular model within the community, featuring 8 billion parameters. It is part of the innovative Llama 3.1 series, which demonstrates revolutionary capabilities in the fields of natural language processing and intelligent dialogue. Architecturally, the model is built on an optimized transformer architecture using advanced Grouped-Query Attention (GQA) technology, providing exceptional scalability for inference. The model was trained on more than 15 trillion tokens from publicly available sources, enabling it to demonstrate deep contextual understanding and generate high-quality responses.

Officially, the model supports a context window of up to 128 thousand tokens and works with eight languages (Russian is not listed, although the model understands it very well). The instruction-tuned version of the model underwent supervised fine-tuning (SFT) and reinforcement learning based on human feedback (RLHF), ensuring a high level of safety and alignment with human preferences.

The potential applications of the model are truly **boundless**: from building intelligent dialogue systems and personal assistants to developing tools for programming and processing large volumes of text. The model supports native function calling (tool calling), making it possible to integrate with external services and create powerful agent-based systems.Thanks to its permissive Llama 3.1 Community License, the model is available for both commercial and research use, making Llama 3.1 8B an ideal choice for developers, researchers, and businesses looking to implement cutting-edge AI technologies into their products and services.

Announce Date: 23.07.2024
Parameters: 8.03B
Context: 131K
Attention Type: Full Attention
VRAM requirements: 19.7 GB using 4 bits quantization
Developer: Meta AI
Transformers Version: 4.42.3
License: LLAMA 3.1

Use our pre-built public endpoints to test inference and explore Llama-3.1-8B capabilities.

Rent your own physically dedicated instance with hourly or long-term monthly billing.

We recommend deploying private instances in the following scenarios:

• maximize endpoint performance,
• enable full context for long sequences,
• ensure top-tier security for data processing in an isolated, dedicated environment,
• use custom weights, such as fine-tuned models or LoRA adapters.

Contact our dedicated neural networks support team at nn@immers.cloud or send your request to the sales department at sale@immers.cloud.